Recent evidence suggests that slow EEG rhythms are involved in post-learning plasticity. However, the relationships between memory consolidation and hippocampal EEG features remain unclear. Here, we assessed the effects of both procedural and declarative learning on qualitative and quantitative measures of sleep by recording stereo-EEG (SEEG) directly from the hippocampus and the neocortex in a...

Deep learning has achieved excellent performance in a wide range of domains, especially speech recognition and computer vision. Relatively less work been done for electroencephalogram (EEG), but there is still significant progress attained the last decade. Due to lack comprehensive topic widely covered survey deep EEG, we attempt summarize recent provide an overview, as well perspectives future...

The aim of this study is to diagnose epileptic seizures by using different machine learning algorithms. For this purpose, the frequency components of the EEG are extracted by using the discrete wavelet transform (DWT) and parametric methods based on autoregressive (AR) model. Both these two feature extraction methods are applied to the input of machine learning classification algorithms such as...

While most EEG based Brain-Computer-Interfaces (BCIs) employ machine learning algorithms for classification, we propose to utilize source localization procedures for this purpose. Although the computational demand is considerably higher, this approach could allow the simultaneous classification of a multitude of conditions. We present an extension of Independent Component Analysis (ICA) based s...

In recent years there has been an increase in the number of portable low-cost electroencephalographic (EEG) systems available to researchers. However, to date the validation of the use of low-cost EEG systems has focused on continuous recording of EEG data and/or the replication of large system EEG setups reliant on event-markers to afford examination of event-related brain potentials (ERP). He...

INTRODUCTION The ability to continuously and unobtrusively monitor levels of task engagement and mental workload in an operational environment could be useful in identifying more accurate and efficient methods for humans to interact with technology. This information could also be used to optimize the design of safer, more efficient work environments that increase motivation and productivity. ...

A Brain Computer Interface (BCI) is a new communication channel allows a person to control special computer applications like a computer cursor or robotic limb through the use of his/her thoughts. BCIs had become an active research area in the last decade. BCI research is based on recording and analyzing electroencephalographic (EEG) data and recognizing EEG patterns associated with various men...

In this paper, we propose a subject-independent approach with deep transfer learning to evaluate the last-night sleep quality using EEG data. To reduce the intrinsic cross-subject differences of EEG data and background noise variations during signal acquisition, we adopt two classes of transfer learning methods to build subject-independent classifiers. One is to find a subspace by matrix decomp...

The active role of sleep in memory consolidation is still debated, and due to a large between-species variation, the investigation of a wide range of different animal species (besides humans and laboratory rodents) is necessary. The present study applied a fully non-invasive methodology to study sleep and memory in domestic dogs, a species proven to be a good model of human awake behaviours. Po...

The brain computer interface (BCI) should be the accessibility solution “par excellence” for interactive and e-learning systems. There is a substantial tradition of research on the human electro encephalogram (EEG) and on BCI systems that are based, inter alia, on EEG measurement. We have not yet seen a viable BCI for e-learning. For many users for a BCI based interface is their first choice fo...